class_docstr: Main class for calculating GageRnR.
docstr: Initialize GageRnR algorithm.
  
  :param numpy.array data:
      The data tha we want to analyse using GageRnR.
      The input should be structured in a 3d array
      n[i,j,k] where i = operator, j = part, k = measurement
docstr_tok: ["Initialize","GageRnR","algorithm",".",":","param","numpy.array","data",":","The","data","tha","we","want","to","analyse","using","GageRnR",".","The","input","should","be","structured","in","a","3d","array","n","[","i",",","j",",","k","]","where","i","=","operator",",","j","=","part",",","k","=","measurement"]
code: def __init__(self, data):
      """Initialize GageRnR algorithm.
  
      :param numpy.array data:
          The data tha we want to analyse using GageRnR.
          The input should be structured in a 3d array
          n[i,j,k] where i = operator, j = part, k = measurement
      """
      super(GageRnR, self).__init__(data)
  
imports: from enum import Enum
  import numpy
  import math
  import scipy.stats
label_desc: Use image node_id 1 to create a new GageRnR object from inherited base classes: Statistics with example: obj = GageRnR(data)